{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f72bfb31",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9aee40d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0., -inf, -inf, -inf, -inf],\n",
       "        [0., 0., -inf, -inf, -inf],\n",
       "        [0., 0., 0., -inf, -inf],\n",
       "        [0., 0., 0., 0., -inf],\n",
       "        [0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn.Transformer.generate_square_subsequent_mask(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b85e865a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3.1 位置编码（不变）\n",
    "class PositionalEncoding(nn.Module):\n",
    "    def __init__(self, d_model, max_len=10, dropout=0.1):\n",
    "        super(PositionalEncoding, self).__init__()\n",
    "        self.dropout = nn.Dropout(p=dropout)\n",
    "\n",
    "        pe = torch.zeros(max_len, d_model)\n",
    "        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)\n",
    "        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))\n",
    "\n",
    "        pe[:, 0::2] = torch.sin(position * div_term)\n",
    "        pe[:, 1::2] = torch.cos(position * div_term)\n",
    "        pe = pe.unsqueeze(0)  # (1,max_len, d_model)\n",
    "        self.register_buffer('pe', pe)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # x: (batch_size, seq_len, d_model)\n",
    "        x = x + self.pe[:,:x.size(0), :]\n",
    "        return self.dropout(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4b98085e",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = torch.randn(2,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "76bd9c0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "p = PositionalEncoding(10, max_len=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c789e601",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[ 0.4853,  1.1017, -1.3299, -0.0182, -0.9216, -0.4581,  0.0000,\n",
       "          -0.0495,  0.4826,  0.0000],\n",
       "         [ 0.0923,  1.1735, -1.6826,  0.8960, -1.4566,  1.8921, -1.7916,\n",
       "           0.3422, -0.2478,  1.9578]]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "abc40cac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d07f1355",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'json' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mjson\u001b[49m\u001b[38;5;241m.\u001b[39mdumps(data,ensure_ascii\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'json' is not defined"
     ]
    }
   ],
   "source": [
    "json.dumps(data,ensure_ascii=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "445ad032",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset,DataLoader\n",
    "import torch\n",
    "import random\n",
    "# 5. 数据集\n",
    "\n",
    "class SequenceDataset(Dataset):\n",
    "    def __init__(self, num_samples=10000, min_len=2, max_len=8, max_value=8):\n",
    "        self.num_samples = num_samples\n",
    "        self.min_len = min_len\n",
    "        self.max_len = max_len\n",
    "        self.max_value = max_value\n",
    "        self.data = self.generate_data()\n",
    "        \n",
    "    def generate_data(self):\n",
    "        data = []\n",
    "        for _ in range(self.num_samples):\n",
    "            seq_len = random.randint(self.min_len, self.max_len)\n",
    "            seq = [random.randint(1, self.max_value) for _ in range(seq_len)]\n",
    "            data.append(seq)\n",
    "        return data\n",
    "    \n",
    "    def __len__(self):\n",
    "        return self.num_samples\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        sequence = self.data[idx]\n",
    "        return torch.tensor(sequence, dtype=torch.long)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b2b53dee",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = SequenceDataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "57168497",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([6, 7, 4, 7])\n",
      "tensor([6, 5, 1])\n",
      "tensor([1, 7, 6])\n",
      "tensor([3, 1, 3, 8])\n",
      "tensor([5, 7, 6, 3, 2])\n",
      "tensor([5, 7, 3])\n",
      "tensor([8, 8, 6, 2, 8, 8, 4])\n",
      "tensor([8, 7])\n",
      "tensor([5, 4, 5, 4, 8])\n",
      "tensor([5, 7, 6, 7, 3, 4, 8, 8])\n",
      "tensor([4, 8, 8, 1, 2])\n",
      "tensor([1, 4])\n",
      "tensor([8, 6])\n",
      "tensor([5, 4, 4, 1, 5, 3])\n",
      "tensor([6, 3])\n",
      "tensor([8, 4, 5, 5, 4, 6, 2, 6])\n",
      "tensor([8, 2, 2, 1, 7, 7, 1])\n",
      "tensor([3, 1, 7, 6])\n",
      "tensor([6, 6, 1, 4])\n",
      "tensor([6, 2, 8, 4])\n",
      "tensor([8, 3, 8, 8, 4])\n",
      "tensor([2, 6, 7, 1, 5])\n",
      "tensor([8, 3, 2, 6, 8, 3])\n",
      "tensor([5, 4, 2, 6])\n",
      "tensor([5, 3, 7, 2])\n",
      "tensor([5, 1, 3, 4, 1, 1, 8])\n",
      "tensor([2, 7, 5, 4, 8, 7, 5, 5])\n",
      "tensor([1, 2, 1, 5])\n",
      "tensor([4, 2, 4, 6, 4, 1, 5, 1])\n",
      "tensor([4, 1, 3, 4, 4, 8, 4])\n",
      "tensor([7, 8, 2, 7])\n",
      "tensor([8, 4, 7])\n",
      "tensor([7, 5, 6, 4, 2])\n",
      "tensor([8, 6])\n",
      "tensor([6, 1, 4, 5, 8, 5, 6])\n",
      "tensor([8, 5, 3, 1])\n",
      "tensor([3, 7, 5, 2, 2])\n",
      "tensor([3, 6, 2, 8, 7, 3, 4, 2])\n",
      "tensor([2, 7, 5])\n",
      "tensor([1, 6, 5, 2])\n",
      "tensor([1, 6, 4, 6, 5, 1, 1])\n",
      "tensor([1, 1, 3, 7, 1, 7])\n",
      "tensor([5, 5, 8, 1, 5, 3])\n",
      "tensor([8, 8, 3, 5, 8, 5, 6])\n",
      "tensor([7, 7, 3, 2, 2, 2, 1])\n",
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      "tensor([5, 3, 5, 4, 2])\n",
      "tensor([3, 7])\n",
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      "tensor([8, 1, 7, 4])\n",
      "tensor([1, 1, 3])\n",
      "tensor([2, 2, 7, 2])\n",
      "tensor([6, 1])\n",
      "tensor([1, 3, 3, 6])\n",
      "tensor([8, 2])\n",
      "tensor([2, 8, 3, 3, 2])\n",
      "tensor([7, 4, 2])\n",
      "tensor([5, 4, 3])\n",
      "tensor([8, 4, 1])\n",
      "tensor([3, 7, 1, 5, 3])\n",
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      "tensor([2, 6, 7, 1, 3, 7])\n",
      "tensor([6, 7, 1, 5, 8, 6, 1])\n",
      "tensor([6, 8, 2, 6, 3, 7, 2])\n",
      "tensor([6, 7, 4, 4, 7, 1, 4])\n",
      "tensor([1, 7, 6, 7])\n",
      "tensor([7, 3, 1])\n",
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      "tensor([7, 2, 3, 6, 3, 2, 2])\n",
      "tensor([7, 3, 1, 2, 3])\n",
      "tensor([2, 4, 8, 1, 8, 8])\n",
      "tensor([6, 3, 8, 2, 6, 3])\n",
      "tensor([7, 6, 3, 1, 1, 7])\n",
      "tensor([3, 5, 1, 7, 4, 5])\n",
      "tensor([2, 6, 1, 4, 2, 3, 6])\n",
      "tensor([8, 7])\n",
      "tensor([7, 3, 4, 6, 2, 8, 7, 3])\n",
      "tensor([2, 3, 5, 4])\n",
      "tensor([7, 2, 7, 4, 1])\n",
      "tensor([6, 1, 6, 1, 2, 5, 3, 3])\n",
      "tensor([6, 8])\n",
      "tensor([2, 7, 4, 8, 3])\n",
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      "tensor([6, 8])\n",
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      "tensor([1, 5, 8, 5, 6, 6, 7, 2])\n",
      "tensor([5, 5, 3, 4, 4, 6, 6, 7])\n",
      "tensor([4, 8, 2, 5, 7, 8, 6, 6])\n",
      "tensor([2, 6, 5, 7, 4, 4, 4])\n",
      "tensor([4, 7])\n",
      "tensor([6, 5, 1, 6])\n",
      "tensor([4, 6])\n",
      "tensor([3, 5, 8, 1, 8, 4, 5])\n",
      "tensor([3, 1, 1, 5, 5, 7, 1, 3])\n",
      "tensor([8, 4, 1, 3])\n",
      "tensor([1, 3, 7, 8, 4, 6, 2, 7])\n",
      "tensor([1, 6, 2])\n",
      "tensor([1, 4, 5, 1])\n",
      "tensor([6, 2, 7, 6, 3])\n",
      "tensor([7, 7, 6, 5, 1, 8, 6, 3])\n",
      "tensor([7, 2, 3])\n",
      "tensor([7, 4, 2, 3])\n",
      "tensor([1, 8, 2, 8, 2, 8])\n",
      "tensor([3, 2, 8, 8])\n",
      "tensor([7, 6, 7, 6])\n",
      "tensor([1, 5, 7, 3, 2, 7, 1])\n",
      "tensor([1, 8, 4, 8, 2])\n",
      "tensor([3, 3, 1, 4, 4, 8, 2, 1])\n",
      "tensor([8, 8, 2, 8, 5, 7, 6])\n",
      "tensor([7, 5, 3, 4, 2])\n",
      "tensor([1, 5, 3, 6])\n",
      "tensor([8, 8, 7, 8, 5, 8, 1, 5])\n",
      "tensor([7, 1, 2, 1, 7])\n",
      "tensor([6, 6])\n",
      "tensor([3, 8, 5, 3, 8, 5])\n",
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      "tensor([7, 6, 7, 7, 6])\n",
      "tensor([5, 7, 7, 2, 3, 3, 7])\n",
      "tensor([6, 6, 4, 5, 4, 2, 1])\n",
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      "tensor([5, 5, 6, 4, 3, 7, 7])\n",
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      "tensor([4, 2, 7, 4, 2, 2, 8])\n",
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      "tensor([6, 3, 5, 2, 6, 5])\n",
      "tensor([8, 7, 4, 4, 5, 7])\n",
      "tensor([5, 3, 5, 4, 6, 3, 7, 5])\n",
      "tensor([1, 6, 8, 1])\n",
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      "tensor([8, 2, 5, 4])\n",
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      "tensor([4, 6, 2, 2])\n",
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      "tensor([2, 3, 8])\n",
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      "tensor([7, 3, 3])\n",
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      "tensor([4, 2, 3, 7])\n",
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      "tensor([6, 4, 3, 5, 4, 1, 2, 6])\n",
      "tensor([1, 1, 1, 6, 2, 4, 7, 2])\n",
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      "tensor([6, 1, 5, 5, 8, 1, 7])\n",
      "tensor([1, 1, 8, 5, 2, 2, 7])\n",
      "tensor([8, 3, 3, 2, 8, 6, 1])\n",
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      "tensor([3, 8, 2])\n",
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      "tensor([8, 6, 3, 5, 5, 2, 5])\n",
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      "tensor([5, 5, 2, 5, 4])\n",
      "tensor([1, 7, 6, 4, 6])\n",
      "tensor([4, 8, 2, 3, 2, 1])\n",
      "tensor([2, 1])\n",
      "tensor([1, 5])\n",
      "tensor([2, 1, 3, 4, 7, 3, 4])\n",
      "tensor([7, 3, 1])\n",
      "tensor([7, 7, 5, 5])\n",
      "tensor([1, 5, 4, 1, 2, 4])\n",
      "tensor([4, 4, 4, 7, 6])\n",
      "tensor([3, 3, 3, 6, 3, 1, 7])\n",
      "tensor([6, 7, 6])\n",
      "tensor([3, 5, 6, 2, 7, 7, 2, 7])\n",
      "tensor([4, 1, 7, 4, 3])\n",
      "tensor([1, 2])\n",
      "tensor([3, 8, 6, 8, 7])\n",
      "tensor([8, 2, 7, 8, 1, 2, 1, 8])\n",
      "tensor([1, 3, 6, 5])\n",
      "tensor([3, 2, 8, 5])\n",
      "tensor([3, 2])\n",
      "tensor([8, 3, 1, 8, 4, 3])\n",
      "tensor([6, 7, 4, 7, 4, 1])\n",
      "tensor([8, 7, 6, 5, 3, 7])\n",
      "tensor([2, 6, 4, 8, 7, 4, 6, 5])\n",
      "tensor([6, 5, 4, 2])\n",
      "tensor([4, 8, 2])\n",
      "tensor([1, 5, 6, 7, 1, 5, 6])\n",
      "tensor([5, 1, 2, 8, 3, 6, 5])\n",
      "tensor([4, 6, 5, 4, 4, 6])\n",
      "tensor([8, 4, 3, 4, 4])\n",
      "tensor([5, 7, 5, 2, 2, 2, 1])\n",
      "tensor([5, 7, 2, 6, 2, 3, 3])\n",
      "tensor([4, 6, 7, 6, 3, 2, 2])\n",
      "tensor([7, 3, 7, 8, 2, 4, 5, 8])\n",
      "tensor([8, 2, 5])\n",
      "tensor([8, 1, 8])\n",
      "tensor([2, 3, 4, 5, 3])\n",
      "tensor([4, 8, 4, 6])\n",
      "tensor([7, 2])\n",
      "tensor([6, 7, 6, 5, 2, 8])\n",
      "tensor([6, 3, 5, 3, 2, 4])\n",
      "tensor([2, 6, 8, 8, 7])\n",
      "tensor([4, 8, 3, 4, 2, 8, 8])\n",
      "tensor([6, 3])\n",
      "tensor([3, 5])\n",
      "tensor([2, 2, 2, 5, 5, 1])\n",
      "tensor([1, 3, 5, 7, 1, 6, 3, 2])\n",
      "tensor([8, 8, 8, 7, 5])\n",
      "tensor([3, 4, 5, 3])\n",
      "tensor([6, 1, 3, 6, 8, 3, 2])\n",
      "tensor([1, 1, 6, 7, 1])\n",
      "tensor([7, 7, 4])\n",
      "tensor([4, 2, 3])\n",
      "tensor([5, 2, 3])\n",
      "tensor([4, 6, 8, 5, 7, 6])\n",
      "tensor([7, 1, 6])\n",
      "tensor([8, 2, 6, 8, 8, 4])\n",
      "tensor([7, 6, 8, 6, 1, 5])\n",
      "tensor([4, 2, 3, 6])\n",
      "tensor([5, 8, 5, 5, 1, 5, 7, 7])\n",
      "tensor([7, 6, 8])\n",
      "tensor([5, 3])\n",
      "tensor([7, 6, 7, 4, 3, 8, 4])\n",
      "tensor([6, 3])\n",
      "tensor([7, 6, 3, 7, 1, 2, 2])\n",
      "tensor([3, 7, 4])\n",
      "tensor([7, 8, 8, 6, 2, 6])\n",
      "tensor([8, 4, 7, 5])\n",
      "tensor([3, 6, 7, 2, 6, 7])\n",
      "tensor([7, 4, 2, 2, 7])\n",
      "tensor([2, 1, 2, 7, 3, 7])\n",
      "tensor([3, 2, 5])\n",
      "tensor([2, 7, 4, 7, 7, 6, 2])\n",
      "tensor([7, 4, 5, 2])\n",
      "tensor([5, 3, 5, 1, 7])\n",
      "tensor([1, 1, 8, 3])\n",
      "tensor([2, 1, 7, 5])\n",
      "tensor([8, 8, 3])\n",
      "tensor([4, 3, 6, 8, 2])\n",
      "tensor([4, 3, 6, 3, 6, 2, 2])\n",
      "tensor([1, 4, 8, 4])\n",
      "tensor([1, 4, 8])\n",
      "tensor([6, 2])\n",
      "tensor([7, 1, 1, 2, 5, 3])\n",
      "tensor([1, 2, 2, 6, 7, 7, 2, 6])\n",
      "tensor([5, 7, 3, 5])\n",
      "tensor([7, 3, 6, 7])\n",
      "tensor([4, 5, 7, 8])\n",
      "tensor([1, 7, 2, 1, 5, 5, 6, 6])\n",
      "tensor([8, 6, 1, 8, 6, 4])\n",
      "tensor([6, 4, 3, 2, 5, 6])\n",
      "tensor([5, 4])\n",
      "tensor([8, 8, 1, 6, 4])\n",
      "tensor([2, 8, 7, 6, 5, 6])\n",
      "tensor([1, 7])\n",
      "tensor([2, 8, 3, 1, 2])\n",
      "tensor([6, 8, 4, 6, 8, 8, 6, 1])\n",
      "tensor([3, 3, 2, 1, 5, 5, 4])\n",
      "tensor([8, 3, 2, 5])\n",
      "tensor([8, 3, 7, 2])\n",
      "tensor([7, 6, 6])\n",
      "tensor([3, 8, 6, 2, 2, 5])\n",
      "tensor([6, 3, 1, 8, 8, 6, 7, 7])\n",
      "tensor([1, 3, 1, 6])\n",
      "tensor([3, 6])\n",
      "tensor([7, 5, 2])\n",
      "tensor([6, 6, 1, 8, 4, 8, 6])\n",
      "tensor([6, 5])\n",
      "tensor([3, 8, 1, 4, 1])\n",
      "tensor([1, 4])\n",
      "tensor([7, 7, 4, 3, 7, 6, 1, 6])\n",
      "tensor([8, 1, 7, 1, 1, 8, 7, 8])\n",
      "tensor([4, 2])\n",
      "tensor([7, 5, 8, 8, 8, 4, 4, 3])\n",
      "tensor([5, 8, 7, 1, 4, 8, 5])\n",
      "tensor([3, 5])\n",
      "tensor([4, 2])\n",
      "tensor([5, 6, 8, 4, 7])\n",
      "tensor([7, 8, 7, 1, 5, 6, 1])\n",
      "tensor([3, 6, 8, 8, 2, 1, 6, 8])\n",
      "tensor([2, 7, 3, 6])\n",
      "tensor([4, 1, 6])\n",
      "tensor([1, 8, 5])\n",
      "tensor([7, 2, 2, 2])\n",
      "tensor([8, 3])\n",
      "tensor([4, 4, 1, 7])\n",
      "tensor([4, 8, 1, 6, 2, 4, 5])\n",
      "tensor([6, 5, 3, 3, 2, 7, 7, 7])\n",
      "tensor([4, 5, 1, 7])\n",
      "tensor([6, 7, 5, 1, 5])\n",
      "tensor([3, 4, 2, 4, 7, 3])\n",
      "tensor([3, 7, 2, 4, 2, 8, 1])\n",
      "tensor([7, 2, 7, 3, 8, 2])\n",
      "tensor([2, 4, 7, 6, 3])\n",
      "tensor([8, 2, 8, 2, 1])\n",
      "tensor([7, 6, 4, 5])\n",
      "tensor([6, 5, 5, 4, 6, 3, 2, 7])\n",
      "tensor([5, 3, 3, 3, 8, 7])\n",
      "tensor([3, 1, 7, 1, 2])\n",
      "tensor([5, 5, 5, 4])\n",
      "tensor([8, 2, 3])\n",
      "tensor([8, 6, 7])\n",
      "tensor([5, 2, 6, 2, 8, 6, 3])\n",
      "tensor([6, 6, 4, 3, 1, 5])\n",
      "tensor([2, 5, 1, 1, 6, 8])\n",
      "tensor([4, 5, 7, 6, 3, 7, 6, 6])\n",
      "tensor([3, 2, 8, 5, 2, 7, 1, 2])\n",
      "tensor([4, 1, 7, 8])\n",
      "tensor([8, 2, 1])\n",
      "tensor([6, 4, 3])\n",
      "tensor([5, 7, 1, 3])\n",
      "tensor([8, 7, 5, 2, 3, 2, 6, 4])\n",
      "tensor([1, 2, 6])\n",
      "tensor([2, 7, 1, 5])\n",
      "tensor([7, 8, 1, 3, 4, 6, 4, 4])\n",
      "tensor([7, 5, 2, 7, 2, 8])\n",
      "tensor([6, 1])\n",
      "tensor([4, 7, 6])\n",
      "tensor([3, 2, 4, 1])\n",
      "tensor([5, 3, 1, 2, 3, 5])\n",
      "tensor([4, 3])\n",
      "tensor([2, 6])\n",
      "tensor([7, 8, 2, 7, 4])\n",
      "tensor([5, 6, 5, 3, 1, 7, 7])\n",
      "tensor([2, 2, 5, 3, 7, 6])\n",
      "tensor([7, 6, 4, 7, 8, 5, 4])\n",
      "tensor([3, 2, 5, 4, 6, 8, 1, 2])\n",
      "tensor([8, 7, 8, 4, 7, 8])\n",
      "tensor([6, 1])\n",
      "tensor([7, 7, 3])\n",
      "tensor([3, 3])\n",
      "tensor([1, 7, 7, 2])\n",
      "tensor([6, 8, 7, 6, 3, 4])\n",
      "tensor([4, 2, 5, 6, 7, 2, 3])\n",
      "tensor([7, 5, 3, 5])\n",
      "tensor([6, 6, 1, 7, 8, 6, 7, 7])\n",
      "tensor([3, 4, 1, 2])\n",
      "tensor([4, 5, 6, 3, 6, 2, 7, 5])\n",
      "tensor([2, 5])\n",
      "tensor([8, 2, 1, 1, 5, 3, 8, 4])\n",
      "tensor([3, 7, 3, 7, 3, 3, 2])\n",
      "tensor([6, 5, 5])\n",
      "tensor([7, 2, 6, 6])\n",
      "tensor([2, 2])\n",
      "tensor([3, 4, 5, 8, 2, 7, 5])\n",
      "tensor([4, 7, 6])\n",
      "tensor([6, 1, 6, 7])\n",
      "tensor([6, 8, 7, 1, 4, 4, 2, 2])\n",
      "tensor([5, 2])\n",
      "tensor([8, 7, 1, 2, 8, 3, 7])\n",
      "tensor([3, 4, 6, 5, 3, 8, 8])\n",
      "tensor([8, 8, 6, 2, 8, 2, 2, 2])\n",
      "tensor([4, 6, 2, 2])\n",
      "tensor([6, 4, 4, 4, 2])\n",
      "tensor([5, 8, 6, 5, 6, 6, 1, 3])\n",
      "tensor([5, 7, 7, 2, 7, 3])\n",
      "tensor([4, 2, 4, 5])\n",
      "tensor([4, 6, 2, 3, 1, 4, 3, 3])\n",
      "tensor([2, 4, 6, 7, 8])\n",
      "tensor([5, 7, 2, 4, 8])\n",
      "tensor([4, 4, 8, 5, 6, 5, 1, 8])\n",
      "tensor([7, 2, 4, 4, 2, 5, 4])\n",
      "tensor([5, 1, 2, 8])\n",
      "tensor([1, 7, 4])\n",
      "tensor([3, 6, 5])\n",
      "tensor([7, 2, 7, 4])\n",
      "tensor([4, 3, 3])\n",
      "tensor([3, 5, 1, 3, 2, 6, 5, 7])\n",
      "tensor([3, 1, 7, 1, 5, 3, 1])\n",
      "tensor([6, 8, 8, 3, 8, 5, 2, 4])\n",
      "tensor([8, 4, 8, 2, 1, 8, 6])\n",
      "tensor([5, 4])\n",
      "tensor([1, 4])\n",
      "tensor([4, 1, 5, 2, 7, 6, 3])\n",
      "tensor([4, 4, 8, 7, 5, 1, 5, 1])\n",
      "tensor([2, 3, 5, 7, 3, 2, 1])\n",
      "tensor([1, 1])\n",
      "tensor([7, 7, 8, 5, 3, 3])\n",
      "tensor([7, 8, 7, 8, 2])\n",
      "tensor([7, 5, 4])\n",
      "tensor([3, 4, 5, 7, 4])\n",
      "tensor([8, 7, 6, 6, 8, 7])\n",
      "tensor([7, 7, 8, 7, 1, 1, 8])\n",
      "tensor([1, 8, 7, 4, 1, 4])\n",
      "tensor([5, 1, 4, 8])\n",
      "tensor([4, 1, 5, 6, 4, 4, 7, 6])\n",
      "tensor([1, 7, 1, 1, 1, 4])\n",
      "tensor([7, 7])\n",
      "tensor([1, 6, 8, 5, 1, 2])\n",
      "tensor([8, 1, 2])\n",
      "tensor([5, 5, 7, 3, 7, 1, 2, 8])\n",
      "tensor([8, 1, 7, 3, 3, 2, 3, 2])\n",
      "tensor([3, 7, 7, 7, 7, 2, 8])\n",
      "tensor([7, 5])\n",
      "tensor([4, 5, 7, 7, 2, 2, 3, 2])\n",
      "tensor([2, 2, 2, 4, 6, 1, 4, 1])\n",
      "tensor([5, 2, 5, 8, 5, 7, 4, 8])\n",
      "tensor([2, 1, 7, 4, 6, 4, 3, 3])\n",
      "tensor([5, 3])\n",
      "tensor([5, 4, 5])\n",
      "tensor([7, 5, 8, 6])\n",
      "tensor([2, 1, 8])\n",
      "tensor([6, 6, 1, 5, 4])\n",
      "tensor([8, 5, 6, 2, 1, 8, 1, 2])\n",
      "tensor([7, 4, 2])\n",
      "tensor([1, 4, 7, 2, 1])\n",
      "tensor([4, 2, 3, 7, 4, 4, 8, 5])\n",
      "tensor([5, 2, 3])\n",
      "tensor([3, 6, 5, 4, 2, 7, 6])\n",
      "tensor([7, 6, 7, 3, 6])\n",
      "tensor([3, 5])\n",
      "tensor([3, 3, 3, 4])\n",
      "tensor([6, 3])\n",
      "tensor([3, 7])\n",
      "tensor([6, 5])\n",
      "tensor([7, 6, 4, 2, 1, 5, 7, 6])\n",
      "tensor([8, 6])\n",
      "tensor([6, 2, 4, 6])\n",
      "tensor([7, 2, 6, 1, 4, 8, 2])\n",
      "tensor([7, 1])\n",
      "tensor([7, 2, 8, 2, 8, 3, 4, 4])\n",
      "tensor([3, 2, 2, 5, 1, 4, 5, 1])\n",
      "tensor([3, 8, 2, 6, 2, 3, 8, 1])\n",
      "tensor([5, 4, 3, 5])\n"
     ]
    }
   ],
   "source": [
    "for _ in dataset:\n",
    "    print(_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "414c54e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3af2ad16",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据预处理\n",
    "def preprocess_sequence(sequence, start_token=0, end_token=9):\n",
    "    encoder_input = sequence.clone()\n",
    "    decoder_input = torch.cat([torch.tensor([start_token]), sequence])\n",
    "    target = torch.cat([sequence, torch.tensor([end_token])])\n",
    "    return encoder_input, decoder_input, target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "843e643c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def collate_fn(batch, start_token=0, end_token=10, pad_token=11):\n",
    "    processed = [preprocess_sequence(seq, start_token, end_token) for seq in batch]\n",
    "    encoder_inputs, decoder_inputs, targets = zip(*processed)\n",
    "    \n",
    "    max_enc_len = max(len(seq) for seq in encoder_inputs)\n",
    "    max_dec_len = max(len(seq) for seq in decoder_inputs)\n",
    "    max_target_len = max(len(seq) for seq in targets)\n",
    "    \n",
    "    padded_enc = []\n",
    "    padded_dec = []\n",
    "    padded_targets = []\n",
    "    \n",
    "    for enc, dec, target in zip(encoder_inputs, decoder_inputs, targets):\n",
    "        pad_enc = torch.cat([enc, torch.full((max_enc_len - len(enc),), pad_token, dtype=torch.long)])\n",
    "        padded_enc.append(pad_enc)\n",
    "        \n",
    "        pad_dec = torch.cat([dec, torch.full((max_dec_len - len(dec),), pad_token, dtype=torch.long)])\n",
    "        padded_dec.append(pad_dec)\n",
    "        \n",
    "        pad_target = torch.cat([target, torch.full((max_target_len - len(target),), pad_token, dtype=torch.long)])\n",
    "        padded_targets.append(pad_target)\n",
    "    \n",
    "    return (\n",
    "        torch.stack(padded_enc),  # 形状: (batch_size, max_enc_len)\n",
    "        torch.stack(padded_dec),  # 形状: (batch_size, max_dec_len)\n",
    "        torch.stack(padded_targets)  # 形状: (batch_size, max_target_len)\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5d351f85",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "train_loader = DataLoader(\n",
    "    dataset, \n",
    "    batch_size=batch_size, \n",
    "    shuffle=True, \n",
    "    collate_fn=lambda x: collate_fn(x, start_token=0, end_token=9, pad_token=10)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "fee636ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32\n",
      "tensor([[ 8,  1, 10, 10, 10, 10, 10],\n",
      "        [ 2,  7,  7,  7,  2, 10, 10],\n",
      "        [ 5,  3,  2,  7,  3,  2, 10],\n",
      "        [ 2,  4, 10, 10, 10, 10, 10],\n",
      "        [ 3,  6,  7,  8,  6,  2,  6],\n",
      "        [ 1,  7,  5, 10, 10, 10, 10],\n",
      "        [ 7,  1,  5,  8,  5,  2, 10],\n",
      "        [ 6,  3,  4,  6,  2,  1,  5],\n",
      "        [ 3,  4, 10, 10, 10, 10, 10],\n",
      "        [ 6,  4, 10, 10, 10, 10, 10],\n",
      "        [ 5,  5,  1, 10, 10, 10, 10],\n",
      "        [ 8,  7,  7,  8,  3,  4,  5],\n",
      "        [ 3,  8, 10, 10, 10, 10, 10],\n",
      "        [ 2,  1,  5,  7,  1, 10, 10],\n",
      "        [ 4,  8,  4, 10, 10, 10, 10],\n",
      "        [ 8,  2,  8,  7,  8, 10, 10],\n",
      "        [ 7,  1, 10, 10, 10, 10, 10],\n",
      "        [ 7,  6,  8, 10, 10, 10, 10],\n",
      "        [ 7,  2, 10, 10, 10, 10, 10],\n",
      "        [ 7,  1,  7,  5,  2,  6, 10],\n",
      "        [ 5,  7,  1, 10, 10, 10, 10],\n",
      "        [ 4,  3,  1,  5,  3,  2, 10],\n",
      "        [ 2,  7,  6,  2,  3, 10, 10],\n",
      "        [ 7,  6,  2,  7,  1,  6, 10],\n",
      "        [ 1,  8,  2,  2,  8, 10, 10],\n",
      "        [ 2,  7,  4,  2,  1,  7, 10],\n",
      "        [ 1,  7,  2,  7,  2,  1,  5],\n",
      "        [ 7,  7,  2, 10, 10, 10, 10],\n",
      "        [ 4,  7,  7,  3,  7, 10, 10],\n",
      "        [ 3,  1, 10, 10, 10, 10, 10],\n",
      "        [ 3,  1,  6,  5,  8, 10, 10],\n",
      "        [ 2,  8,  1,  5, 10, 10, 10]])\n",
      "tensor([[ 0,  8,  1, 10, 10, 10, 10, 10],\n",
      "        [ 0,  2,  7,  7,  7,  2, 10, 10],\n",
      "        [ 0,  5,  3,  2,  7,  3,  2, 10],\n",
      "        [ 0,  2,  4, 10, 10, 10, 10, 10],\n",
      "        [ 0,  3,  6,  7,  8,  6,  2,  6],\n",
      "        [ 0,  1,  7,  5, 10, 10, 10, 10],\n",
      "        [ 0,  7,  1,  5,  8,  5,  2, 10],\n",
      "        [ 0,  6,  3,  4,  6,  2,  1,  5],\n",
      "        [ 0,  3,  4, 10, 10, 10, 10, 10],\n",
      "        [ 0,  6,  4, 10, 10, 10, 10, 10],\n",
      "        [ 0,  5,  5,  1, 10, 10, 10, 10],\n",
      "        [ 0,  8,  7,  7,  8,  3,  4,  5],\n",
      "        [ 0,  3,  8, 10, 10, 10, 10, 10],\n",
      "        [ 0,  2,  1,  5,  7,  1, 10, 10],\n",
      "        [ 0,  4,  8,  4, 10, 10, 10, 10],\n",
      "        [ 0,  8,  2,  8,  7,  8, 10, 10],\n",
      "        [ 0,  7,  1, 10, 10, 10, 10, 10],\n",
      "        [ 0,  7,  6,  8, 10, 10, 10, 10],\n",
      "        [ 0,  7,  2, 10, 10, 10, 10, 10],\n",
      "        [ 0,  7,  1,  7,  5,  2,  6, 10],\n",
      "        [ 0,  5,  7,  1, 10, 10, 10, 10],\n",
      "        [ 0,  4,  3,  1,  5,  3,  2, 10],\n",
      "        [ 0,  2,  7,  6,  2,  3, 10, 10],\n",
      "        [ 0,  7,  6,  2,  7,  1,  6, 10],\n",
      "        [ 0,  1,  8,  2,  2,  8, 10, 10],\n",
      "        [ 0,  2,  7,  4,  2,  1,  7, 10],\n",
      "        [ 0,  1,  7,  2,  7,  2,  1,  5],\n",
      "        [ 0,  7,  7,  2, 10, 10, 10, 10],\n",
      "        [ 0,  4,  7,  7,  3,  7, 10, 10],\n",
      "        [ 0,  3,  1, 10, 10, 10, 10, 10],\n",
      "        [ 0,  3,  1,  6,  5,  8, 10, 10],\n",
      "        [ 0,  2,  8,  1,  5, 10, 10, 10]])\n",
      "tensor([[ 8,  1,  9, 10, 10, 10, 10, 10],\n",
      "        [ 2,  7,  7,  7,  2,  9, 10, 10],\n",
      "        [ 5,  3,  2,  7,  3,  2,  9, 10],\n",
      "        [ 2,  4,  9, 10, 10, 10, 10, 10],\n",
      "        [ 3,  6,  7,  8,  6,  2,  6,  9],\n",
      "        [ 1,  7,  5,  9, 10, 10, 10, 10],\n",
      "        [ 7,  1,  5,  8,  5,  2,  9, 10],\n",
      "        [ 6,  3,  4,  6,  2,  1,  5,  9],\n",
      "        [ 3,  4,  9, 10, 10, 10, 10, 10],\n",
      "        [ 6,  4,  9, 10, 10, 10, 10, 10],\n",
      "        [ 5,  5,  1,  9, 10, 10, 10, 10],\n",
      "        [ 8,  7,  7,  8,  3,  4,  5,  9],\n",
      "        [ 3,  8,  9, 10, 10, 10, 10, 10],\n",
      "        [ 2,  1,  5,  7,  1,  9, 10, 10],\n",
      "        [ 4,  8,  4,  9, 10, 10, 10, 10],\n",
      "        [ 8,  2,  8,  7,  8,  9, 10, 10],\n",
      "        [ 7,  1,  9, 10, 10, 10, 10, 10],\n",
      "        [ 7,  6,  8,  9, 10, 10, 10, 10],\n",
      "        [ 7,  2,  9, 10, 10, 10, 10, 10],\n",
      "        [ 7,  1,  7,  5,  2,  6,  9, 10],\n",
      "        [ 5,  7,  1,  9, 10, 10, 10, 10],\n",
      "        [ 4,  3,  1,  5,  3,  2,  9, 10],\n",
      "        [ 2,  7,  6,  2,  3,  9, 10, 10],\n",
      "        [ 7,  6,  2,  7,  1,  6,  9, 10],\n",
      "        [ 1,  8,  2,  2,  8,  9, 10, 10],\n",
      "        [ 2,  7,  4,  2,  1,  7,  9, 10],\n",
      "        [ 1,  7,  2,  7,  2,  1,  5,  9],\n",
      "        [ 7,  7,  2,  9, 10, 10, 10, 10],\n",
      "        [ 4,  7,  7,  3,  7,  9, 10, 10],\n",
      "        [ 3,  1,  9, 10, 10, 10, 10, 10],\n",
      "        [ 3,  1,  6,  5,  8,  9, 10, 10],\n",
      "        [ 2,  8,  1,  5,  9, 10, 10, 10]])\n"
     ]
    }
   ],
   "source": [
    "for a,b,c in train_loader:\n",
    "    # a,b = d\n",
    "    # print(b)\n",
    "\n",
    "    \n",
    "    if len(a[0])!=8:\n",
    "        print(len(a))\n",
    "        print(a)\n",
    "        print(b)\n",
    "        print(c)\n",
    "        break\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "605f2b31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "inf"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.inf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "c4e643ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1.2000, 1.2000, 1.2000],\n",
       "        [1.2000, 1.2000, 1.2000],\n",
       "        [1.2000, 1.2000, 1.2000]])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.full((3,3),1.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "85ea2cf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0., -inf, -inf, -inf, -inf],\n",
       "        [0., 0., -inf, -inf, -inf],\n",
       "        [0., 0., 0., -inf, -inf],\n",
       "        [0., 0., 0., 0., -inf],\n",
       "        [0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.triu(torch.full((5, 5), float('-inf')), diagonal=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "afe3e71e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-inf, -inf, -inf, -inf, -inf],\n",
       "        [-inf, -inf, -inf, -inf, -inf],\n",
       "        [-inf, -inf, -inf, -inf, -inf],\n",
       "        [-inf, -inf, -inf, -inf, -inf],\n",
       "        [-inf, -inf, -inf, -inf, -inf]])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.full((5, 5), float('-inf'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "ddc77553",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.float32"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.triu(torch.ones(5,5),diagonal=1).dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "0946d721",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=torch.randn(32,7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "832dd935",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.full((x.size(0),x.size(1)),0,dtype=torch.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "494b7137",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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